Our current understanding of gene regulatory networks does not adequately utilize information from individual protein-DNA interactions and the millions of regulatory elements identified by high-throughput functional assays. New strategies are needed to incorporate data from each of these experimental scales, and to leverage the orthogonal datasets to understand how each regulatory element is involved in directing complex gene expression responses. The objective of our proposal is to develop statistical models to learn the underlying patterns of complex interactions involved in transcription regulation across the genome. While the genome-proximal response to glucocorticoid (GC) treatment is an ideal model system, the methods developed will be applicable to studying any complex regulatory network. The goal of Aim 1 will be to comprehensively characterize the first 12 hours of the GC response using genome-wide methods to quantify expression, TF binding, histone modifications, chromatin accessibility, three-dimensional chromatin structure, and the function of regulatory elements. The outcome will be the most comprehensive and coordinated molecular description of a human regulatory network ever produced. All data will be generated with the highest possible quality standards, and will be submitted pre-publication and without restriction into the public domain. Aim 2 will integrate that data into a nonparametric and hierarchical Bayesian model of the GC response network (GCRN). That model will able to produce functional predictions for each individual regulatory element while also generalizing across genes to reveal shared principles of gene regulation. Aim 3 will validate and reduce uncertainty in the model. That will be accomplished by combining statistical experimental design approaches with multiplex genome and epigenome engineering to iteratively and optimally resolve the most uncertain aspects of the model. The outcome will be a validated and predictive model of the GCRN that will be useful to design customized genomic responses. Aim 4 will demonstrate the use of the resulting model through reprogramming the GCRN to minimize the response of genes associated with metabolism while maintaining the response of genes associated with inflammation and immunity. The outcome will be a derived cell line with a custom programmed GC response. That cell line will have immediate use for studying individual aspects of the GC response; and the approach used to design and realize the customized response will have broad implications for the study of other transcriptional response networks. The overall result of this project will be a mechanistic and actionable understanding of the principles through which individual DNA sequences contribute to the GCRN; a general and transferrable multi-scale modeling approach to study any complex regulatory network; and the novel ability to genetically reprogram transcriptional response networks to study their individual components. We anticipate that that outcome will have broad positive impact on both experimental and computational fields of biomedical research.